Exploring the Situational Awareness of Humans inside Autonomous Vehicles

With increasing automated driving capabilities of commercial vehicles, the study of safe and smooth occupant-vehicle interaction and control transitions is key. In this study, we focus on the development of contextual, semantically meaningful representations of driver and vehicle states, which can then be used to determine the appropriate timing and conditions for transfer of control between driver and vehicle. To this end, we lay out the specifications of the vehicle platform required to conduct such a study, and explore some of the sensors and algorithms that may be needed to produce useful and observable high level cues (features) to make such decisions. These features encode different aspects of the driver state, pertaining to the face, hands, foot and upper body of the driver. Finally, we evaluate these features on their capability of capturing the state of a driver, and demonstrate a strong agreement between these features and a humans' notion of situational awareness.